检索结果-内蒙古大学图书馆

Solution of multi-objective optimal power flow using gravitational search algorithm

IET GENERATION TRANSMISSION & DISTRIBUTION 2012年第8期6卷 751-763页

作者： Bhattacharya, A. Roy, P. K. Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India

This article presents application of an efficient and reliable heuristic technique inspired by swarm behaviours in nature namely, gravitational search algorithm (GSA) for solution of multi-objective optimal power flow (OPF) problems. GSA is based on the Newton's law of gravity and mass interactions. In the proposed algorithm, the searcher agents are a collection of masses that interact with each other using laws of gravity and motion of Newton. In order to investigate the performance of the proposed scheme, multi-objective OPF problems are solved. A standard 26-bus and IEEE 118-bus systems with three different individual objectives, namely fuel cost minimisation, active power loss minimisation and voltage deviation minimisation, are considered. In multi-objective problem formulation fuel cost and loss;fuel cost and voltage deviation;fuel cost, loss and voltage deviation are minimised simultaneously. Results obtained by GSA are compared with mixed integer particle swarm optimisation, evolutionary programming, genetic algorithm and biogeography-based optimisation. The results show that the new GSA algorithm outperforms the other techniques in terms of convergence speed and global search ability.

关键词： GSA algorithm search problems evolutionary programming Optimisation techniques reliable heuristic technique multiobjective problem formulation fuel cost voltage deviation minimisation swarm behaviours multiobjective OPF problems genetic algorithm multiobjective optimal power flow solution active power loss minimisation IEEE bus systems fuel cost minimisation Newton law of gravity and mass interactions biogeography-based optimisation gravitational search algorithm load flow mixed integer particle swarm optimisation Combinatorial mathematics Power systems

来源：评论

学校读者我要写书评

暂无评论

A novel crazy swarm optimized economic load dispatch for various types of cost functions

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2008年第4期30卷 242-253页

作者： Roy, Ranjit Ghoshal, S. P. SV Natl Inst Technol Dept Elect Engn Surat 395007 India Natl Inst Technol Dept Elect Engn Durgapur 713209 W Bengal India

Economic load dispatch (ELD) is the scheduling of generators to minimize the total operating cost depending on equality and inequality constraints. The transmission line loss also is to be kept as minimum as possible. So, the problem is of multi-objective optimization. The authors have studied the comparative effectiveness of GA, Improved fast EP (IFEP) and various particle swarm optimizations (PSO) reported in the literature and a novel particle swarm algorithm namely CRAZYPSO for such multi-objective optimization in two test cases. The first test case deals with some well-known Benchmark functions and then the second case deals with a general power system having 40 thermal generating units with non-monotonically increasing cost functions with valve point loadings and other constraints. The generators are interconnected through lossy transmission lines. The proposed method out performs and provides true global optimal solutions as compared to other existing techniques for economic load dispatch. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： crazypso economic load dispatch evolutionary programming GA HPSO vurmfipso

来源：评论

学校读者我要写书评

暂无评论

Optimal battery energy storage planning and control strategy for grid modernization using improved genetic algorithm

引用

ENERGY REPORTS 2023年 9卷 236-241页

作者： Mansuwan, Kannathat Jirapong, Peerapol Thararak, Panida Chiang Mai Univ Dept Elect Engn Fac Engn Chiang Mai 50200 Thailand Chiang Mai Univ Grad Sch Chiang Mai 50200 Thailand

The flexible operation of battery energy storage systems (BESS) to support electricity grid modernization requires optimal planning and an efficient control strategy. This paper proposes the optimal allocation of BESS with photovoltaic systems for microgrids to enhance grid reliability and flexibility. An adaptive BESS control strategy is developed to allow the BESS to operate in both redundant and peak shaving modes. An evolutionary programming-based genetic algorithm implemented with an optimal power flow control is proposed to determine the BESS's optimal location, sizing, and charge/discharge operation. The objective function is to maximize the benefits of energy loss reduction and peak shaving enhancement while minimizing the installation cost. The proposed approach is conducted with MATLAB and DIgSILENT PowerFactory software, which uses an intelligent automatic data exchange process to solve the optimal solution. A practical 22 kV grid-connected microgrid of Thailand is used as a test system to demonstrate the effectiveness of the planning and control strategy for the BESS installation. The simulation test results show that the proposed optimal BESS allocation and control strategy can significantly reduce the microgrid's energy loss and peak demand and increase energy shaving leading to enhanced grid resiliency and reliability. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 7th International Conference on Renewable Energy and Conservation, ICREC, 2022.

关键词： Battery energy storage system evolutionary programming Genetic algorithm Optimal allocation Photovoltaic system

来源：评论

学校读者我要写书评

暂无评论

Distribution network reconfiguration based on artificial network reconfiguration for variable load profile

引用

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2020年第5期28卷 3013-3035页

作者： Youssef, Hesham Hanie Mokhlis, Hazlie Abu Talip, Mohamad Sofian Al Samman, Mohammad Muhammad, Munir Azam Mansor, Nurulafiqah Nadzirah Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur Malaysia

Network reconfiguration is a process to change the open-switches in distribution system for a minimum power loss. In the past, metaheuristic techniques were applied widely for network reconfiguration with consideration of a fixed loading profile. When the loading changes, the current configuration may not be the optimal one. Thus, the technique needs to be executed to find a new optimal configuration based on the latest loading. The process is time-consuming since metaheuristic techniques commonly require high computational times and produces inconsistent results. Therefore, this paper proposes a network reconfiguration technique based on artificial neural network (ANN) for variable loading conditions. The proposed ANN model is tested on IEEE 33-bus, IEEE 69-bus, and IEEE-118 bus systems. The test results indicate the efficiency of the proposed technique in three aspects: processing time, simple structure, and high accuracy.

关键词： Distribution system network reconfiguration artificial neural networks evolutionary programming

来源：评论

学校读者我要写书评

暂无评论

Simultaneous network reconfiguration and DG using EP method

引用

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2015年第11期25卷 2577-2594页

作者： Dahalan, Wardiah Mohd Mokhlis, Hazlie Ahmad, Rohaya Abu Bakar, Abdul Halim Musirin, Ismail Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia Univ Kuala Lumpur Dept Marine Elect Engn Perak 32000 Malaysia Univ Malaya UM Power Energy Dedicated Adv Ctr Kuala Lumpur 59990 Malaysia Univ Teknol MARA Shah Alam 40450 Selangor Malaysia

Distributed generation (DG) sources have recently become more significant in the distribution system due to the increasing demand of electricity energy. Choosing the most suitable size of DG is an essential step to ensure tremendous effect on its operation and power system control. An optimal DG size will result in the reduction of electrical power loss as well as the cost of operation. Power loss reduction can also be achieved through optimal network reconfiguration. By considering optimal network reconfiguration and DG sizing, the effectiveness and efficiency of the system can be improved while having lower power loss in the power system. Therefore, this paper presents the simultaneous method to identify the optimal network reconfiguration and DG sizing using evolutionary programming. The main goal of this work was to reduce the power loss and increase the voltage profile, while at the same time fulfill the operating constraints. The developed method was tested on 33-bus and 69-bus system with different operating conditions. The test results showed a great improvement of power loss reduction and overall voltage profile of the system. The implementation of reconfiguration and DG technique in 33-bus and 69-bus systems which were operated simultaneously had shown tremendous improvement in terms of the number of iterations to reach the optimal value of power losses and DG sizing. Copyright (C) 2014 John Wiley & Sons, Ltd.

关键词： distributed generation optimization techniques power loss reduction reconfiguration evolutionary programming

来源：评论

学校读者我要写书评

暂无评论

Approximations with evolutionary pursuit

引用

SIGNAL PROCESSING 2003年第3期83卷 465-481页

作者： da Silva, ARF Univ Nova Lisboa Dept Elect Engn P-2825 Monte De Caparica Portugal

In recent years there has been growing interest in nonlinear signal approximations and atomic decomposition of functions. We propose a new approach to optimize overcomplete decompositions from several dictionaries. The methodology, referred to as evolutionary pursuit, relies on evolutionary computation techniques to optimize well-adapted approximations. Searching for the best approximation for a given signal is viewed as a stochastic optimization process. Stochastic perturbation parameters are introduced to improve both stability and robustness in the process of combining components from multiple dictionaries. The utility of these parameters in the analysis is justified in terms of the theory of frames, and tested in applications. The proposed method is applied to a collection of representative experimental tests, and the results compared with equivalent results from alternative approaches. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： signal approximations atomic decompositions wavelets evolutionary programming matching pursuit basis pursuit

来源：评论

学校读者我要写书评

暂无评论

Comparison of AI techniques to solve combined economic emission dispatch problem with line flow constraints

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2010年第6期32卷 592-598页

作者： Raglend, I. Jacob Veeravalli, Sowjanya Sailaja, Kasanur Sudheera, B. Kothari, D. P. Noorul Islam Univ Sch Elect Sci Kumaracoil 629180 India Vellore Inst Technol Sch Elect Sci Vellore 632004 Tamil Nadu India Vellore Inst Technol Univ SMIEEE FNASC FNAE Vellore 632014 Tamil Nadu India

A comparative study has been made on the solutions obtained using combined economic emission dispatch (CEED) problem considering line flow constraints using different intelligent techniques for the regulated power system to ensure a practical, economical and secure generation schedule. The objective of the paper is to minimize the total production cost of the power generation. Economic load dispatch (ELD) and economic emission dispatch (EED) have been applied to obtain optimal fuel cost of generating units. Combined economic emission dispatch (CEED) is obtained by considering both the economic and emission objectives. This bi-objective CEED problem is converted into single objective function using price penalty factor approach. In this paper, intelligent techniques such as genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), differential evolution (DE) are applied to obtain CEED solutions for the IEEE 30-bus system and 15-unit system. This proposed algorithm introduces an efficient CEED approach that obtains the minimum operating cost satisfying unit, emission and network constraints. The proposed algorithm has been tested on two sample systems viz the IEEE 30-bus system and a 15-unit system. The results obtained by the various artificial intelligent techniques are compared with respect to the solution time, total production cost and convergence criteria. The solutions obtained are quite encouraging and useful in the economic emission environment. The algorithm and simulation are carried out using Matlab software. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Economic load dispatch Economic emission dispatch Combined economic emission dispatch Genetic algorithm evolutionary programming Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Unmanned aerial vehicles cooperative tracking of moving ground target in urban environments

引用

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2008年第5期31卷 1360-1371页

作者： Shaferman, Vitaly Shima, Tal Technion Israel Inst Technol Dept Aerosp Engn IL-32000 Haifa Israel

The problem of autonomous tracking of aground moving target in an urban terrain is studied. In the investigated scenario, the target is tracked from multiple unmanned aerial vehicles using gimballed or body-fixed sensors under the constraints of terrain occlusions and airspace limitations. Information regarding the occlusions may be available a priori from a database or may be provided to the system by the operator based on his understanding of the environment. To ensure flyable trajectories, the unmanned aerial vehicles' dynamic constrains must be taken into account. A methodology is proposed for solving in real time a general class of such problems by casting the tracking task as a cooperative motion planning problem. Because of the computational complexity of the problem, a stochastic search method (genetic algorithm) is proposed for finding in real time monotonically improving solutions. An important attribute of the proposed solution approach is its scalability and, consequently, applicability to large-sized problems. For testing the algorithm, it was implemented in a high-fidelity simulation test bed using a visual database of an actual city., The viability of using the algorithm is shown using a Monte Carlo study. It is envisioned that automating this part of a ground moving target tracking problem will considerably reduce operators' workload and dramatically improve mission performance in such real-life problems.

关键词： UAV Sensors Airspace Motion Planning Genetic Algorithm Search Algorithm evolutionary programming Autopilot Optimal Control Mobile Robots

来源：评论

学校读者我要写书评

暂无评论

evolutionary algorithms in computer-aided molecular design

引用

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 1996年第4期10卷 337-358页

作者： Clark, DE Westhead, DR PROTEUS MOL DESIGN LTD MACCLESFIELD SK11 0JL CHESHIRE ENGLAND

In recent years, search and optimisation algorithms inspired by evolutionary processes have been applied with marked success to a wide variety of problems in diverse fields of study. In this review, we survey the growing application of these 'evolutionary algorithms' in one such area: computer-aided molecular design. In the course of the review, we seek to summarise the work to date and to indicate where evolutionary algorithms have met with success and where they have not fared so well. In addition to this, we also attempt to discern some future trends in both the basic research concerning these algorithms and their application to the elucidation, design and modelling of chemical and biochemical structures.

关键词： genetic algorithms evolutionary programming evolution strategies drug design molecular modelling protein folding

来源：评论

学校读者我要写书评

暂无评论

ERBF network with immune clustering

引用

Journal of Systems Engineering and Electronics 2004年第3期15卷 315-318页

作者：宫新保臧小刚周希朗 DepartmentofElectronicEngineering ShanghaiJiaotongUniversityShanghai200030P.R.China

Based on immune clustering and evolutionary programming(EP), a hybrid algorithm to train the RBF network is proposed. An immune fuzzy C-means clustering algorithm (IFCM) is used to adaptively specify the amount and initial positions of the RBF centers according to input data set; then the RBF network is trained with EP that tends to global optima. The application of the hybrid algorithm in multiuser detection problem demonstrates that the RBF network trained with the algorithm has simple network structure with good generalization ability.

关键词： immune clustering algorithm evolutionary programming RBF network.

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：